Robot Scavenger Hunt: A Standardized Framework for Evaluating Intelligent Mobile Robots (2016)
Shiqi Zhang, Dongcai Lu, Xiaoping Chen, and Peter Stone
In recent years, many different types of intelligent mobile robots have been developed in research and industrial labs. Although there are significant differences in both hardware and software over these robots, many of them share a common set of AI capabilities, e.g., planning, learning, vision and natural language processing. At the same time, almost all of them are equipped with traditional robotic capabilities such as mapping, localization, and navigation. However, to date it has been difficult to compare and contrast their capabilities in any controlled way. The main goal of the Robot Scavenger Hunt is to provide a standardized framework that includes a set of standardized tasks for evaluating the AI and robotic capabilities of medium-sized intelligent mobile robots. Compared to existing benchmarks, e.g., RoboCup@Home1, Robot Scavenger Hunt aims at evaluations in larger spaces (multi-floor buildings vs. rooms) over longer periods of time (hours vs. minutes) while interacting with real human residents.
In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), New York City, USA, July 2016.

Peter Stone Faculty pstone [at] cs utexas edu
Shiqi Zhang Postdoctoral Alumni szhang [at] cs utexas edu